The present invention relates to semiconductor manufacturing technology, and more particularly, to a method and system of non-uniformity pattern identification.
A conventional semiconductor factory typically includes the requisite fabrication tools necessary to process semiconductor wafers for a particular purpose, such as photolithography, chemical-mechanical polishing, or chemical vapor deposition. During manufacture, the semiconductor wafer passes through a series of process steps, which are performed by various fabrication tools. For example, in the production of an integrated semiconductor product, the semiconductor wafer passes through up to 600 process steps. The costs for such automated production are influenced to a great extent by the question as to how well and efficiently the manufacturing process can be monitored or controlled, so that the ratio of defect-free products to the overall number of products manufactured (i.e., yield ratio) achieves as great a value as possible. The individual process steps, however, are subject to fluctuations and irregularities, which in the worst case may mean, for example, the defect of a number of chips or the entire wafer. Therefore, each individual process step must be carried out as stably as possible in order to ensure an acceptable yield after the completed processing of a wafer. The fluctuations, irregularities and instability of a process step will cause so-called non-uniformity patterns, reducing yield. There may be various types of with-in-wafer (WIW) non-uniformity patterns of particular data, e.g., in-line process manufacturing parameters, wafer acceptance test (WAT) parameters, circuit probing (CP) test parameters and the like, subject to various fabrication issues. In the past, simple calculation algorithms, such as range value, and standard deviation, with predetermined thresholds have been used to determine whether a wafer suffers from WIW non-uniformity. Identification of WIW non-uniformity patterns, however, is done by human effort. The labor-intensive nature of WIW non-uniformity pattern identification using conventional means severely hinders efficiency. Therefore, a need exists for a system and method of non-uniformity pattern identification, to not only improve efficiency, but also provide a more effective and reliable result.